How Many Rays Do I Need for Monte Carlo Optimization? DI7g- h8`
While it is important to ensure that a sufficient number of rays are traced to "D?:8!\!
distinguish the merit function value from the noise floor, it is often not necessary to kyu
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trace as many rays during optimization as you might to obtain a given level of A,]%*kg2
accuracy for analysis purposes. What matters during optimization is that the Z$: iq
changes the optimizer makes to the model affect the merit function in the same way to# N>VfD
that the overall performance is affected. It is possible to define the merit function so A7=k 9|
that it has less accuracy and/or coarser mesh resolution than meshes used for (?lKedA>2
analysis and yet produce improvements during optimization, especially in the early hvOl9 W>
stages of a design. 7V-'>< )gI
A rule of thumb for the first Monte Carlo run on a system is to have an average of at J:oAzBFpA
least 40 rays per receiver data mesh bin. Thus, for 20 bins, you would need 800 rays OGn-~
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on the receiver to achieve uniform distribution. It is likely that you will need to ":V,&o9n
define more rays than 800 in a simulation in order to get 800 rays on the receiver. HAc1w]{(
When using simplified meshes as merit functions, you should check the before and J0,;F9<C#X
after performance of a design to verify that the changes correlate to the changes of 1 JB~G7
the merit function during optimization. As a design reaches its final performance =Bw2{]w
level, you will have to add rays to the simulation to reduce the noise floor so that *PF=dx<8
sufficient accuracy and mesh resolution are available for the optimizer to find the vw[i.af
best solution.
